Single nucleotide polymorphisms and use of same in predicting male-specific prenatal loss

ABSTRACT

The present invention is directed to a panel of single nucleotide polymorphisms (SNPs) in specific genes that serve as biomarkers for sex-specific prenatal loss of a conceptus or embryo. There is provided herein methods and reagents for assessing the specific SNPs in those genes. The method useful in applying these SNPs in predicting an increased risk of prenatal loss is also disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of the U.S. Utility application Ser. No. 12/386,106 filed Apr. 14, 2009, which claims the benefit under 35 U.S.C. §119(e) to U.S. Provisional Applications Nos. 61/124,111 filed Apr. 14, 2008 and 61/132,634 filed Jun. 20, 2008, the content of which is incorporated by reference herein in their entirety.

BACKGROUND OF THE INVENTION

Prenatal loss is a common occurrence. The survival probability of human conceptions from fertilization to term is estimated to be less than 25% (Roberts & Lowe, 1975). Vatten et al. described a primary male-to-female ratio of 120-165:100 at the time of fertilization, but the ratio decreases to 106:100 at the time of birth. This suggests that prenatal loss concerns males more than that of females, albeit the underlying mechanism is not clear.

There have always been interests and efforts in discovering the contribution of genetic factors to pregnancy loss. A conventional approach is to use population genetics to assess sex-specific prenatal loss. This population genetic approach involves genotyping women experiencing repeated pregnancy loss. Although some positive findings were obtained, the results have been inconsistent. Even if positive findings were obtained, whether miscarriages represent the whole spectrum of repeated pregnancy loss is doubtful. Miscarriages represent only a fraction of the total prenatal loss, and thus rendering the past studies underpowered. Thus, the population genetic approach is suboptimal at best.

Single nucleotide polymorphism (SNP) is a common form of genetic polymorphisms. SNP may influence gene functions and modifies an individual's susceptibility to diseases. Almost any diseases have a genetic component in its etiology and most are being unraveled in genetic association studies. In some instances, a single SNP may be sufficient to confer susceptibility, while in others multiple SNPs may act jointly to influence disease susceptibility. An estimated 20 million SNPs are present in human genome. This astronomical number precludes individual screening one at a time because of the huge work and cost.

To the best of the present inventors' knowledge, there are no reliable genetic markers for prenatal selection (i.e., fetal survival) that have clinical utility. Genetic tests used in in vitro fertilization (IVF) clinics in pre-implantation genetic screening do not contain a genetic marker to predict the survival probability of pregnancy but screens for chromosomal abnormality.

Accordingly, there is a continuing need for a genetic marker to predict the probability of pregnancy success as well as sex-specific prenatal selection. The need for a reliable SNP biomarker for sex-specific prenatal selection is expected to have utility in the application in IVF and infertility clinics.

BRIEF SUMMARY OF THE INVENTION

The present invention is based on the present discovery of particular SNPs in selected genes that represent biomarker candidates in regulating prenatal development for male and female offspring. In accordance with the present invention, the SNPs influence prenatal selection individually or in particular combinations of genotypes, and hence contribute to differential viability of male and female embryos or fetuses. There is disclosed herein SNPs that contribute to differential viability of male and female embryos or fetuses by their different frequencies in healthy newborn males and females.

In one aspect, the present invention provides a panel of such SNPs that predict sex-specific prenatal selection and methods of using these SNPs in assessing the propensity of prenatal loss probability.

In one aspect, the present invention provides a candidate gene approach and identifying a subset of single nucleotide polymorphisms (SNPs) that is useful to predict the probability of prenatal loss for a given offspring.

In one aspect, the present invention provides a method for predicting prenatal loss of a conceptus or embryo, comprising the steps of: (a) providing a biological sample; (b) isolating nucleic acid from said sample; and (c) assessing the presence of a SNP selected from the group consisting of RXRB rs2076310, HLA-DQA1 rs1142316, HLA-DRA rs7192, HSPA1B rs1061581, GTF2H4 rs3909130, HIST1H1T rs198844, IFNG rs2069727, IL-6 rs1800796, KLRK1 rs10772266, KLRK1 rs2617160, KLRK1 rs2617171, TMPRSS6 rs733655, and HMOX1 rs2071748, wherein the presence of said SNP is indicative of an increased risk of prenatal loss for male conceptus or embryo. Preferably, the biological sample is derived from a conceptus or amniocentesis. The nucleic acid may be genomic DNA, mRNA or isolated DNA.

In one aspect, the present invention provides a method whereby an assessing step for SNPs is performed by polymerase chain reaction-restriction fragment length polymorphism assay or TaqMan allelic discrimination assay. The assessing step is performed preferably by a process which comprises subjecting said nucleic acid to an PCR amplification flanking the region of said SNP.

In one aspect, the present invention provides a method for predicting prenatal loss of a conceptus or embryo by assessing the presence of a combination of SNPs of HLA-DQA1 rs1142316, HLA-DRA rs7192, and HSPA1B rs1061581, wherein the presence of such a combination is indicative of an increased risk of prenatal loss for male conceptus or embryo.

In one aspect, the present invention provides a method of predicting prenatal loss of a conceptus or embryo by assessing the presence of a combination of SNPs of KLRK1 rs10772266, KLRK1 rs2617160, and KLRK1 rs2617171 wherein the presence of such a combination is indicative of an increased risk of prenatal loss for male conceptus or embryo.

In one aspect, the present invention provides a method for predicting prenatal loss of a conceptus or embryo, comprising the steps of: (a) providing a biological sample; (b) isolating nucleic acid from said sample; and (c) assessing the presence of a SNP further selected from the group consisting of RXRB rs421446, BRD2 rs635688, HLA-E rs1264456, IRF4 rs12203592, IRF4 rs872071, LIF rs929271, TP53 rs1042522, MDM2 rs2279744, SLC11A2 rs422982, SLC40A1 rs1439814, and RRM2 rs1130609.

In one aspect, the present invention provides a method for predicting prenatal loss of a conceptus or embryo by assessing the presence of a combination of SNPs of LIF rs929271, TP53 rs1042522, and MDM2 rs2279744, wherein the presence of such a combination is indicative of an increased risk of prenatal loss for male conceptus or embryo.

In one aspect, the present invention provides a method for predicting prenatal loss of a conceptus or embryo by assessing the presence of a combination of SNPs of IRF4 rs12203592, and IRF4 rs872071, wherein the presence of such a combination is indicative of an increased risk of prenatal loss for male conceptus or embryo.

In yet another aspect, the present invention provides a method of predicting prenatal survival probability of a prospective offspring of a couple, comprising the steps of: (a) providing a biological sample; (b) isolating nucleic acid from said sample; and (c) assessing the presence of a SNP selected from the group consisting of RXRB rs2076310, HLA-DQA1 rs1142316, HLA-DRA rs7192, HSPA1B rs1061581, GTF2H4 rs3909130, HIST1H1T rs198844, IFNG rs2069727, IL-6 rs1800796, KLRK1 rs10772266, KLRK1 rs2617160, KLRK1 rs2617171, TMPRSS6 rs733655, and HMOX1 rs2071748, wherein the presence of said SNP is indicative of a decreased prenatal survival probability of a prospective offspring.

In one aspect, the present invention provides a method for predicting a decreased prenatal survival probability of a prospective offspring by assessing the presence of a combination of SNPs of KLRK1 rs10772266, KLRK1 rs2617160, and KLRK1 rs2617171, wherein the presence of such a combination is indicative of a decreased prenatal survival probability of a prospective offspring.

In one aspect, the present invention provides a method for predicting a decreased prenatal survival probability of a prospective offspring of a couple, comprising the steps of: (a) providing a biological sample; (b) isolating nucleic acid from said sample; and (c) assessing the presence of a SNP further selected from the group consisting of RXRB rs421446, BRD2 rs635688, HLA-E rs1264456, IRF4 rs12203592, IRF4 rs872071, LIF rs929271, TP53 rs1042522, MDM2 rs2279744, SLC11A2 rs422982, SLC40A1 rs1439814, and RRM2 rs1130609.

In one aspect, the present invention provides a method of predicting a decreased prenatal survival probability of a prospective offspring by assessing the presence of a combination of SNPs of LIF rs929271, TP53 rs1042522, and MDM2 rs2279744, wherein the presence of such a combination is indicative of a decreased prenatal survival probability of a prospective offspring.

In one aspect, the present invention provides a method of predicting a decreased prenatal survival probability of a prospective offspring by assessing the presence of a combination of SNPs of IRF4 rs12203592, and IRF4 rs872071, wherein the presence of such a combination is indicative of a decreased prenatal survival probability of a prospective offspring.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts the genomic location of the single nucleotide polymorphisms (SNPs) evaluated for their values to predict sex-specific prenatal selection by genotyping healthy newborns.

FIG. 2 depicts the individual and additive predictive power of the independent predictive subset of single nucleotide polymorphisms (SNPs) as biomarkers for sex-specific prenatal loss.

DETAILED DESCRIPTION OF THE INVENTION

The present inventors cured the prior art deficiency and used a novel approach to identify biomarkers in predicting sex-specific prenatal loss. The present invention provides genetic markers in male and female newborns. The present invention provides comparison of genotype frequencies that provide clues for the involvement of genes in prenatal selection. Selected gene candidate in biologically plausible targets in HLA complex, immune system-related genes (NKG2D and cytokines) and iron-related genes were genotyped in healthy newborns. The present inventors discovered that specific single nucleotide polymorphisms (SNPs) in these genes represent good predictors for sex-specific prenatal selection, and that the prenatal selection acts strongly against male fetuses.

DEFINITIONS

Various terms used throughout this specification shall have the definitions set forth herein.

The term “polymorphism” refers to the occurrence of two or more alternative genomic sequences or alleles between or among different genomes or individuals.

The term “polymorphic” refers to the condition in which two or more variants of a specific genomic sequence found in a population.

The term “polymorphic site” is the locus at which the variation occurs. A polymorphic site generally has at least two alleles, each occurring at a significant frequency in a selected population. A polymorphic locus may be as small as one base pair, in which case it is referred to as single nucleotide polymorphism (SNP). The first identified allelic form is arbitrarily designated as the reference, wild-type, common or major form, and other allelic forms are designated as alternative, minor, rare or variant alleles.

The term “genotype” refers to a description of the alleles of a gene contained in an individual or sample.

The term “single nucleotide polymorphism” (“SNP”) refers to a site of one nucleotide that varies between alleles.

The term “oligonucleotide” is used interchangeable with “primer” or “polynucleotide.”

The term “primer” refers to an oligonucleotide that acts as a point of initiation of DNA synthesis in a PCR reaction. A primer is usually about 15 to about 35 nucleotides in length and hybridizes to a region complementary to the target sequence.

The term “probe” refers to an oligonucleotide that hybridizes to a target nucleic acid in a PCR reaction. Target sequence refers to a region of nucleic acid that is to be analyzed and comprises the polymorphic site of interest.

The term “TaqMan allelic discrimination assay” (also known as the 5′ nuclease PCR assay) is a technology that exploits the 5′-3′ nuclease activity of Taq DNA polymerase to allow direct detection of the PCR product by the release of a fluorescent report as a result of PCR. The TaqMan allelic discrimination assay permits discrimination between the alleles of a two-allele system. It represents a sensitive and rapid means of genotyping SNPs.

The term “functional SNPs” refers to those SNPs that produce alterations in gene expression or in the expression or function of a gene product, and therefore are most predictive of a possible clinical phenotype. The alterations in gene function caused by functional SNPs may include changes in the encoded polypeptide, changes in mRNA stability, binding of transcriptional and translation factors to the DNA or RNA, and the like.

The term “PCR-RFLP” refers to polymerase chain reaction-restriction fragment length polymorphism. PCR-RFLP is technique to detect a variation in the DNA sequence of a genome by breaking the DNA into pieces with restriction enzymes and analyzing the size of the resulting fragments by gel electrophoresis. PCR-RFLP is one type of genotyping for detecting SNP by visualization of fragments on a gel following restriction endonuclease digestion of the PCR product.

The term “repeated pregnancy loss” is defined clinically as failure of established pregnancies before a live birth more than two times.

The term “an increased risk of prenatal loss” refers to a situation where the survival probability of a male offspring is reduced compared to that of a female. For purposes of this application, it refers to an odds ratio <0.50 (i.e., more than two-fold increased risk) and has a statistically significance of P ≦0.05 indicate strongly increased risk.

The term “95% confidence interval” (or “95% CI”) refers to the range of values surrounding the odds ratio (OR) within which the true value is believed to lie with 95% certainty.

The term “conceptus” refers to the embryo in the uterus, during the early stage of pregnancy. The term “embryo” refers an unborn human baby, especially in the first eight weeks from conception, after implantation but before all the organs are developed. For purposes of this application, “conceptus” and “embryo” are used interchangeably.

The term “Hardy-Weinberg equilibrium” refers to a principle that allele and genotype frequencies in a population remain constant; that is, they are in equilibrium—from generation to generation unless specific disturbing influences are introduced. Those disturbing influences include non-random mating, mutations, selection, limited population size, random genetic drift and gene flow. In the simplest case of a single locus with two alleles: one allele is denoted “A” and the other “a” and their frequencies are denoted by p and q; freq(A)=p; freq(a)=q; p+q=1. According to the Hardy-Weinberg principle, when the population is in equilibrium, then we will have freq(AA)=p² for the AA homozygotes in the population, freq(aa)=q² for the aa homozygotes, and freq(Aa)=2pq for the heterozygotes.

The term “haplotype tagging SNPs” (htSNPs) refers to a subset of SNPs in each gene that provides sufficient information about genetic variation in a gene as genotyping all of the SNPs in a gene. They basically represent other SNPs in their vicinity and make the others redundant in terms of providing additional information about genetic variation.

The term “linkage disequilibrium” refers to the non-random association in population genetics of alleles at two or more loci. Linkage disequilibrium describes a situation in which some combinations of alleles or genetic markers occur more or less frequently in a population than would be expected from a random formation of haplotypes from alleles based on their frequencies. Non-random associations between polymorphisms at different loci are measured by the degree of linkage disequilibrium.

The term “odds ratio” (OR) refers to the ratio of the frequency of the disease in individuals having a particular marker (allele or polymorphism) to the frequency of the disease in individuals without the marker (allele or polymorphism).

The term “multivariable analysis” refers to an analysis used to assess the independent contribution of each of the multiple risk factors that contribute to a disease condition. That is, multivariable analysis helps to determine the most informative minimal set of independent (uncorrelated) multiple risk markers (variables). In situations where two SNPs from the same gene show statistically significant association, but when tested together in a multivariable analysis, if they are correlated, one of them loses significance and the other one is called an independent marker. The one that is no longer significantly associated is still useful in estimation of the risk in the absence of any other marker, but its association is only due to its correlation with a stronger marker. Since human diseases are often influenced by multiple genes, it is usual to find associations with many SNPs from many genes. In this case, a multivariable analysis is used to eliminate any redundancy.

The term “adjusted odds ratio” refers to an odds ratio that is adjusted with another factor (e.g., age). When all independent risk markers are analyzed together in a multivariable analysis, the odds ratio for each marker may be slightly different from the odds ratios obtained from analysis of each SNP on its own. These new odds ratios are called adjusted odds ratios. Since no SNP acts on its own in reality, these adjusted odds ratios represent a more realistic estimate of the risk. These are odds ratios calculated by statistical algorithms that take into account individual contributions of any other risk marker (variable) included in the multivariable analysis.

In one embodiment, the present invention provides a panel of SNPs that exhibit associations with sex-specific prenatal selection. The SNPs identified are present in specific candidate genes. In another embodiment, the present invention provides a method of using genotyping approach to identify a panel of SNPs listed in Table 4 out of all the 244 SNPs listed in Table 1.

In accordance with the present invention, one of a skilled artisan understands that SNPs have two alternative alleles, each corresponds to a nucleotide that may exist in the chromosome. Thus, a SNP is characterized by two nucleotides out of four (A, C, G, T). An example would be that a SNP has either allele C or allele T at a given position on each chromosome. This is shown as C>T or C/T. The more commonly occurring allele is shown first (in this case, it is C) and called the major, common or wild-type allele. The alternative allele that occurs less commonly instead of the common allele (in this case, it is T) is called minor, rare or variant allele. To avoid confusion, in this patent application, we adopted to use wild-type and variant allele to define the common and rare alleles. Since humans are diploid organisms meaning that each chromosome occurs in two copies, each individual has two alleles at a SNP. These alleles may be two copies of the same allele (CC or TT) or they may be different ones (CT). The CC, CT and TT are called genotypes. Among these CC and TT are characterized by having two copies of the same allele and are called homozygous genotypes. The genotype CT has different alleles on each chromosome and is a heterozygous genotype. Individuals bearing homozygote or heterozygote genotypes are called homozygote and heterozygote, respectively.

The present inventors discovered that by examining genotype frequencies of polymorphisms in newborns, clues may be obtained as to which genes are involved in prenatal loss. This can be achieved by comparing genotype frequencies in newborn males and females for sex-specific selection.

In one embodiment, the present invention provides a method of using genotype data rather than sequence data, SNPs are identified to support the findings in the association study. Hardy-Weinberg equilibrium (HWE) and Ewens-Watterson (E-W) tests are used in the present genotype-based tools to search evidence for selection.

HWE tests check the agreement between observed genotype frequencies and expected frequencies calculated from observed allele frequencies. A perfect agreement is expected when several assumptions are met. One of the assumptions is the absence of selection. A statistically significant result in the goodness-of-fit test examining the agreement suggests disequilibrium. The cause for this is change in genotype distribution in the population is usually selection. In practice, however, the most common cause for Hardy-Weinberg disequilibrium is genotyping errors. It is often possible to distinguish between selection and genotyping error when HWE is violated. Genotyping errors are unlikely to be selective. This means if HWE is violated in males but not in females, it points out towards a selective event that has occurred exclusively in males.

In one embodiment, the present invention provides of method of using a statistical test (e.g., HWE) to obtain evidence for sex-specific prenatal selection using genotype frequencies in male and female newborns. The statistical tests for HWE often yield significant results. The present invention also provides other statistical tests (e.g., E-W test) to complement the HWE test. One of ordinary skill in the art would recognize that detail of the HWE test is publicly available in Haploview version 4.0 (http://www.broad.mit.edu/mpg/haploview).

In one embodiment, the present invention provides another statistical test (i.e., E-W test) that can be used when population genetic data is available. E-W test shares some common feature as that of HWE test. When the E-W test attains a statistically significance, it is an indication of selection. Besides association tests, the present inventors tested the data with both HWE and E-W tests in order to obtain additional evidence for prenatal selection in genotype data generated from healthy newborns. For the E-W test, a publicly available PopGen software version 1.32 (http://www.ualberta.ca/˜fyeh) is used.

In accordance with the present invention, there is disclosed an optimal approach that utilizes genotyping to provide direct evidence for sex-specific prenatal loss. In this approach, if a genotype has a deleterious effect on the prenatal development of a male offspring, newborn males will have a reduced frequency for that genotype compared with female newborns. The present approach has advantages of examining healthy newborns who survived the prenatal selection till the end of pregnancy, thus providing summary data regarding all forms of prenatal selection (i.e., implantation failure, embryonic development errors, and fetal loss). The present approach is therefore superior to other approaches (e.g., population genetics) in prenatal selection that focuses on miscarriages. Studying couples experiencing repeated pregnancy loss to find genetic markers is disadvantageous because miscarriages represent a minority of total prenatal loss.

In one embodiment, the present invention provides a method of utilizing an individual SNP to predict susceptibility to prenatal loss of males. In accordance with the present invention, the assessing techniques to determine the presence of a SNP are known in the field of molecular genetics. Further, many of the methods involve amplification of nucleic acids. (See, PCR Technology: Principles and Applications for DNA Amplification (Ed. H. A. Erlich, Freeman Press, NY, N.Y., 1992), and Current Protocols in Molecular Biology, Ausubel, 1999).

In one embodiment, the detection of the presence of a SNP in a particular gene is genotyping. One of the many suitable genotyping procedures is the TaqMan allelic discrimination assay. In this assay, one may utilize an oligonucleotide probe labeled with a fluorescent reporter dye at the 5′ end of the probe and a quencher dye at the 3′ end of the probe. The proximity of the quencher to the intact probe maintains a low fluorescence for the reporter. During the PCR reaction, the 5′ nuclease activity of DNA polymerase cleaves the probe, and separates the dye and quencher. Thus resulting in an increase in fluorescence of the reporter. Accumulation of PCR product is detected directly by monitoring the increase in fluorescence of the reporter dye. The 5′ nuclease activity of DNA polymerase cleaves the probe between the reporter and the quencher only if the probe hybridizes to the target and is amplified during PCR. The probe is designed to straddle a target SNP position and hybridize to the nucleic acid molecule only if a particular SNP allele is present.

Genotyping is performed using oligonucleotide primers and probes. Oligonucleotides may be synthesized and prepared by any suitable methods (such as chemical synthesis), which are known in the art. Oligonucleotides may also be conveniently available through commercial sources. One of the skilled artisans would easily optimize and identify primers flanking the gene of interest in a PCR reaction. Commercially available primers may be used to amplify a particular gene of interest for a particular SNP. A number of computer programs (e.g., Primer-Express) is readily available to design optimal primer/probe sets. It will be apparent to one of skill in the art that the primers and probes based on the nucleic acid information provided (or publically available with accession numbers) can be prepared accordingly.

The labeling of probes is known in the art. The labeled probes are used to hybridize within the amplified region during the amplification region. The probes are modified so as to avoid them from acting as primers for amplification. The detection probe is labeled with two fluorescent dyes, one capable of quenching the fluorescence of the other dye. One dye is attached to the 5′ terminus of the probe and the other is attached to an internal site, so that quenching occurs when the probe is in a non-hybridized state.

As appreciated by one of skill in the art, other suitable genotyping assays may be used in the present invention. This includes hybridization using allele-specific oligonucleotides, primer extension, allele-specific ligation, sequencing, electrophoretic separation techniques, and the like. Exemplary assays include 5′ nuclease assays, molecular beacon allele-specific oligonucleotide assays, and SNP scoring by real-time pyrophosphate sequences.

Determination of the presence of a particular SNP is typically performed by analyzing a nucleic acid sample present in a biological sample obtained from an individual. Biological sample is derived from a conceptus or amniocentesis. The nucleic acid sample comprises genomic DNA, mRNA or isolated DNA. The nucleic acid may be isolated from blood samples, cells or tissues. Protocols for isolation of nucleic acid are known. When RNA is used, the analysis can be performed by first reverse-transcribing the target RNA using, for example, a viral reverse transcriptase, and then amplifying the resulting cDNA.

PCR-RFLP represents an alternative genotyping method used in the invention. PCR-RFLP can yield unambiguous results provided that there is a suitable endonuclease that will cut the amplified PCR product containing a SNP if it contains one of the alternative nucleotides but not the others. Results of PCR-RFLP may be achieved by visualization of fragments on a gel following restriction endonuclease digestion of the PCR product. Thus, a fragment of DNA containing the SNP is first amplified using two oligonucleotides (primers) and is subject to digestion by the variant allele-specific restriction endonuclease enzyme. If the fragment contains the variant allele it is cut into two or more pieces and in the absence of the variant allele, the PCR product remains intact. By visualizing the end-products of the digestion process by agarose or polyacrylamide gel electrophoresis, the presence or absence of the variant allele is easily detected. Other suitable methods that are known in the art such as single-base extension assay, oligonucleotide ligation assay, DNA microarray, pyrosequencing, high-resolution melting method, denaturing high-performance liquid chromatography, mass spectrometry, microsphere-based suspension array platform (Luminex)-based assays and the like can be used in the present invention to detect the presence of SNP.

In one embodiment, the present invention provides a panel of individual SNPs that are useful in predicting sex-specific prenatal loss. This panel of SNPs includes RXRB rs2076310, HLA-DQA1 rs1142316, HLA-DRA rs7192, HSPA1B rs1061581, GTF2H4 rs3909130, HIST1H1T rs198844, IFNG rs2069727, IL-6 rs1800796, KLRK1 rs10772266, KLRK1 rs2617160, KLRK1 rs2617171, TMPRSS6 rs733655, and HMOX1 rs2071748.

In another embodiment, the present invention further provides an additional panel of individual SNPs useful in predicting sex-specific prenatal loss. This additional panel includes RXRB rs421446, BRD2 rs635688, HLA-E rs1264456, IRF4 rs12203592, IRF4 rs872071, LIF rs929271, TP53 rs1042522, MDM2 rs2279744, SLC11A2 rs422982, SLC40A1 rs1439814, and RRM2 rs1130609.

In another embodiment, the present invention provides a method of utilizing multiple SNPs that would exert joint effects and alter the individual's susceptibility to sex-specific prenatal loss.

In one embodiment, the present invention provides a method of using haplotype tagging SNPs (i.e., htSNPs). hsSNPs represent a cluster of SNPs in their vicinity; together, they provide additional information about genetic variation. The present invention provides a method of using the htSNP approach. When there is no already known functional SNP available in a candidate gene, the present invention provides a method of using htSNPs to predict individual's susceptibility to sex-specific prenatal loss. The goal is to use functional SNPs that are known to affect either the function or expression of a gene. The use of functional SNPs may yield a positive association. On the other hand, a non-functional SNP may also be a marker to predict the outcome.

Haplotype tagging SNPs are capable of representing other SNPs. This is because of a phenomenon called linkage disequilibrium (LD). An htSNP and other SNPs tagged or represented by the htSNP form a group that are equally informative when genotyped individually. Any pair of SNPs that are in linkage disequilibrium may provide the same information. If one SNP is associated with a disease condition, the other SNP is similarly associated with the same disease condition. This generates a situation in genetic association studies where an association may be replicated by using a different SNP that is in the linkage disequilibrium with the original SNP. Accordingly, the SNPs in the present panel may be replaced by other SNPs to yield the same information. The linkage disequilibrium information is available in public resources such as HapMap (http://www.hapmap.org) or genome variation server (GVS: http://gvs.gs.washington.edu/GVS).

In one embodiment, the present invention provides a panel of SNPs, when in combination, produces a synergistic effect on sex-specific prenatal loss. While an individual SNP alone has no effect, the combined SNPs together exert a significant effect. In an exemplary embodiment, the presence of a combination of SNPs of HLA-DQA1 rs1142316, HLA-DRA rs7192, and HSPA1B rs1061581 is indicative of a sex-specific prenatal loss. In another exemplary embodiment, the presence of a combination of SNPs of KLRK1 rs10772266, KLRK1 rs2617160, and KLRK1 rs2617171 is indicative of a sex-specific prenatal loss.

In yet another exemplary embodiment, the presence of a combination of LIF rs929271, TP53 rs1042522, and MDM2 rs2279744 is indicative of a sex-specific prenatal loss.

In another exemplary embodiment, the presence of a combination of SNPs of IRF4 rs12203592, and IRF4 rs872071 is indicative of a sex-specific prenatal loss.

The SNP's individual and combined effects on sex-specific prenatal loss against male are similar to that in decreasing prenatal survival probability of a prospective offspring.

As will be apparent to one of skill in the art, one utility of the present invention relates to the field of in vitro fertilization (IVF). After a fertilized egg undergoes cell division to become multiple cell stages (i.e., 8-cell stage), the cells can be separated. The single cell can be used to perform multiple genotyping. This can be achieved by whole genome amplification (WGA). The technology for amplifying DNA from a single cell is known. The resulting whole genome amplified DNA can be used for PCR-based genotyping. The use of WGA in pre-implantation genetic testing on single cell biopsies from 8-cell stage embryo is known in the art. (See, e.g., Zhang et al. Proc. Natl. Acad. Sci. USA 89(13): 5847-51 (1992), Snabes et al., Proc. Natl. Acad. Sci. USA 91(13): 6181-5 (1994), and Coskun et al., Prenat. Diagn. 27(4): 297-302 (2007). After the genotyping assessment of the presence of specific SNPs, a physician can thereby predict the risk of sex-specific prenatal loss or chance of prenatal survival probability of a prospective offspring. The present invention provides a useful tool in deciding to implant a particular fertilized embryo based on the genotyping results.

EXPERIMENTAL STUDIES Example 1 Characteristics of Population Samples

To obtain evidence for sex-specific prenatal selection, we examined genotype frequencies in male and females newborns and compared these frequencies for differences by statistical methods. Any difference found suggested differential viability for male and female fetuses bearing that genotype.

The population samples consisted of 388 cord blood samples form 201 girls and 187 boys. The cord blood samples were collected in EDTA-containing tubes. White blood cells were isolated using standard protocols. DNA was extracted from white blood cells using standard phenol-chloroform extraction method or equivalent methods. DNA samples were re-suspended in double distilled H₂O at 100 nanograms per microliter and kept frozen at −20° C. until used for genotyping. Further details of the samples are provided in detailed experimental procedures section.

Table 1 lists all of the 244 SNPs from the candidate genes we selected to test for their predictive value for prenatal selection. The table provides the gene name, the SNP ID number (beginning with rs) as listed in National Center for Biotechnology Information (NCBI) Entrez SNP (http://www.ncbi.nlm.nih.gov/sites/entrez?db=snp), chromosomal location and the position in the chromosome as nucleotide number beginning from the tip of the short arm of a chromosome.

Each one of the 244 SNPs from our candidate genes were genotyped in newborns and genotype frequencies were compared between male and female newborns. Any difference between the frequencies was considered to be an indication of differential viability of male and female offspring.

Example 2 Selection of Genes for Testing their Role in Prenatal Selection

To the best of the present inventors' knowledge, despite few published reports (Healey et al., 2000; Denschlag et al., 2004; Pietrowski et al., 2005; Goodman et al., 2009), there are no genetic polymorphisms for prediction prenatal selection (i.e., fetal survival) in clinical use. Past studies designed to correlate genetic markers to prenatal selection using couples who had experienced recurrent miscarriages. However, these miscarriages represent only a fraction of the total prenatal loss, and thus rendering the past studies underpowered.

The present inventors used a new approach. We noted that male-to-female ratio is high at the time of fertilization in humans; however, the male-to-female ratio diminishes by the time of birth (i.e., from up to 165 males-to-100 females to 106 males-to-100 females). We postulated and tested the hypothesis that prenatal selection is sex-specific; that is, prenatal selection acts strongly against male fetuses. To test this hypothesis, we examined genetic markers in male and female newborns.

While any gene may have a role in embryonic or fetal viability, we stratified the genes for the probability of their involvement in prenatal selection and used a candidate gene approach. Besides known physiologic roles of genes, we also exploited our own findings in childhood leukemia since susceptibility to leukemia and prenatal selection share genetic risk markers. Furthermore, childhood leukemia is more common in males and since we explored markers for sex-specific prenatal selection, we included leukemia risk markers. Most of these markers are from the HLA complex and iron regulatory genes but also included selected cytokine genes IFNG, IL-10, IL-6 and LIF (See, Table 1 and FIG. 1). These two groups of genes represent plausible gene candidates for prenatal selection.

We chose to examine additional gene candidates. These include heme oxygenase I (i.e., HMOX1), leukemia inhibitory factor (i.e., LIF) and natural killer cell receptor (i.e., NKG2D). We analyzed selected polymorphisms of these relevant genes in the potential genetic marker list (See, Table 1, and FIG. 1).

Furthermore, we examined selected polymorphisms of TP53, IL-6, IL-10, IL-1B. These genes have been suggested to associate with prenatal selection (TP53) and repeated pregnancy loss (HMOX1, 1L-6, 1L-10, IL-1B).

Example 3 Genotypings of Single Nucleotide Polymorphisms

Genotypings of SNPs were achieved by a variety of methods. They usually provide equivalent results. The choice was based on availability of the necessary instruments and expertise, budget available for the study and convenience. Our choice of method was TaqMan allelic discrimination assay for ordinary SNP genotyping. All TaqMan assays were purchased from ABI (California) (See Table 6).

When TaqMan allelic discrimination assay was not possible to use, we chose an alternative method. This happened for MDM2 rs2279744, HSPA1B rs1061581 and HLA-DQA1 rs1142316. For these polymorphisms, we used a PCR based restriction fragment length polymorphism assay. The details of these methods used to genotype polymorphisms within our candidate genes are given in the detailed experimental procedures section.

Table 2 shows the 24 SNPs either showed an individual difference in genotype frequencies between male and female healthy newborns or contributed to a combination of regional genotype combinations that showed frequency differences. The gene name, SNP ID number, alternative name for the SNP according to Genome Variation Society (HGV), when available, SNP location within the gene and nucleotide change are shown.

Example 4 Natural Killer Cell Receptor KLRK1 (NKG2D) and Prenatal Loss

The major role played by NK cells in maternal tolerance to fetus is well recognized (Sargent et al, 2006; Hanna et al, 2006). It has been shown that maternal immune tolerance to developing offspring, which is immunologically foreign to maternal immune system, is achieved by natural killer cells. Natural killer cell activity is regulated by multiple molecules and receptor systems. Among those, the most powerful is the NKG2D receptor encoded by the KLRK1 gene (Raulet D H, 2003). The KLRK1 gene is polymorphic and these polymorphisms are associated with cancer susceptibility (Hayashi et al, 2006).

We obtained genotype and allele frequencies in the healthy newborns. In overall analysis, all loci were in HWE with the exception of rs10772266 (P=0.004) and in sex-specific analysis, this distortion was evident in boys only (P=0.02) suggesting a selection event affecting males during prenatal period. In most other SNPs, HWE was mildly violated in boys (rs1049174, rs2617160, rs2617170, rs2617171) while all SNPs remaining in equilibrium in girls. The E-W neutrality test showed statistically significant evidence for selection only for rs10772266 and only in boys.

The same KLRK1 haplotype that was described as associated with low natural cytotoxic activity in Japan (Hayashi et al, 2006) was the commonest haplotype also in the Caucasian sample analyzed here. Notably, all the SNPs within the haplotype block described by Hayashi et al. showed differences between boys and girls in their frequencies. Inspection of genotype frequencies revealed that there were differences in heterozygous frequencies between boys and girls and boys had consistently lower rates for heterozygous genotypes. The stronger violation of HWE in boys suggested that selection was stronger in boys and the statistical assessment showed that the deviation was heterozygote deficit in boys rather than excess in girls.

The magnitude of deficit in boys was 18.4% for rs2617170, which is a coding region variant (N104S) in KLRC4 immediately 3′ to the KLRK1 gene. This variant is also an htSNP for the 3′ end of KLRK1 in the HapMap project. Since the sample was healthy newborns, we interpreted this finding as suggestive evidence for the involvement of KLRK1 in feto-maternal interactions and possibly in sex-specific prenatal selection. The strong evidence for a functional role played by KLRK1 in feto-maternal interactions is the demonstration of the secretion of soluble MHC class I chain-related molecules (MIC) by placental trophoblastic cells to counteract the maternal NK cell activity by blocking KLRK1 receptors.

Example 5 Genetic Markers in HLA-Complex that Correlate with Prenatal Selection

We identified three genetic markers that bear high correlation with prenatal selection in homozygosity representing main lineages of HLA haplotypes. These genetic markers are: (i) HSPA1B rs1061581; (ii) HLA-DRA rs7192; and (iii) HLA-DQA1 rs1142316. The major alleles of these SNPs characterize the ancestral HLA-DRB4 lineage (i.e., HLA-DR4, HLA-DR7 and HLA-DR9). The minor alleles of these SNPs characterize the HLA-DRB3 lineage (i.e., HLA-DR3, HLA-DR11/12 and HLA-DR13/14). The frequency in male newborns who were homozygote for either the major alleles or minor alleles of the three SNPs was 5.9%. In contrast, the frequency in female newborns was 14.6%. The comparison between the frequencies in male and female newborns was statistically significant (P=0.006). The more than two-fold deficit in homozygosity of SNPs in male newborns is consistent with the hypothesis that there exists a prenatal selection against male offspring bearing these SNP haplotypes.

We hypothesized that transcription factors encoded within the HLA complex may also be relevant in prenatal selection. There are several embryo-expressed and evolutionarily conserved transcription factor genes within the HLA complex. Of these, SNPs from RXRB, BRD2 and GTF2H4 showed statistically significant frequency differences between male and female newborns and RXRB2 and GTF2H4 retained their significance in the multivariable model as independent markers of prenatal selection. Besides these, HLA-E and HIST1H1T also showed frequency differences between males and females with HIST1H1T remaining in the final model (these results are presented in Tables 3 and 4).

Example 6 Iron-Related Gene Polymorphisms and that Correlate with Prenatal Selection

Iron is a required element for cellular proliferation. One iron-related gene HMOX1 (heme oxygenase 1) has been shown to affect recurrent miscarriage susceptibility (Denschlag et al., 2004). This association was, however, with a promoter region microsatellite marker, which is not as easy to type as a SNP marker. We studied the HMOX1 gene SNPs to search associations with prenatal selection.

The SNP rs2071748 showed a sex-specific frequency difference in newborns. The frequency in male newborns who were homozygote for the minor allele of rs2071748 was 14.7%. In contrast, the frequency in female newborns was 22.1%. The difference between the frequencies in male and female newborns reached borderline statistical significance (P=0.06). The ˜two-fold deficit in homozygosity of the SNP in male newborns is consistent with the hypothesis that there is a prenatal selection against male fetuses bearing this SNP genotype.

The present inventors screened iron regulatory pathway genes and detected associations with sex-specific prenatal loss (OR≦0.67 or P≦0.05). These SNPs were from the genes SLC11A2 (also known as NRAMP2), SLC40A1, RRM2 and TMPRSS6. The SNPs and accompanying statistics are listed in Table 3.

Example 7 Leukemia Inhibitory Factor (LIF) and Sex-Specific Prenatal Selection

We examined LIF and its natural genetic variation to search for variants as markers for prenatal loss. LIF interacts with TP53 and TP53 interacts with MDM2. We found functional polymorphisms of TP53 its interaction with MDM2 to produce joint effects.

Individually LIF, TP53 and MDM2 SNPs did not show a statistically significant association with sex-specific prenatal loss. The only suggestive association was with wild-type homozygosity for the LIF SNP rs929271, which yielded an odds ratio of 0.71 (P=0.10). However, when all three SNPs were analyzed together, there was a significant finding. The combination of having wild-type homozygote genotypes in each of the three SNPs at LIF, TP53 and MDM2 showed a deficit in newborn males compared with girls (OR=0.30, 95% CI=0.12 to 0.75; P=0.009). This finding confirmed the involvement of LIF in the success of pregnancy and also the interactions with TP53/MDM2 as expected form their biologic interaction. The present investigation showed the sex-specificity of this effect in that having the wild-type homozygote genotypes at these three SNPs has a deleterious effect for male offspring and such offspring have three-times reduced chance of reaching the end of pregnancy.

Example 8 Associations of Cytokine Genes Interferon-Gamma (IFNG) and Interleukin-6 (IL-6) Polymorphisms With Sex-Specific Prenatal Loss

We examined IFNG SNP in our candidate SNPs because of its sex-specific expression patterns. To investigate their association with sex-specific prenatal selection, we genotyped selected SNPs from IL-6, IL-10 and IFNG genes. Two of those, IFNG rs2069727 and IL-6 rs1800796, showed different genotype frequencies between male and female newborns. These results are shown in Table 3. The effect of these SNPs was strong enough to remain in the multivariable model in the presence of other markers of prenatal loss. The adjusted odds ratios were less than 0.50 for both SNPs (Table 4) meaning reduced chance of survival for male offspring during pregnancy.

Example 9 Heterozygote Advantage in Sex-Specific Prenatal Selection

In this series of study, we examined heterozygosity at all SNPs for its effect on sex-specific prenatal selection. As already mentioned in different sections above, HLA-E rs1264456 individually, IRF4 SNPs rs12203592 and rs872071 in combination, and KLRK1 SNPs rs2617160 and rs2617171 in combination showed reduced frequencies in male newborns compared with female newborns. The HLA-E and IRF4 SNPs were not retained in the final model but the two KLRK1 region SNPs in combination with rs10772266 remained statistically significant.

Altogether, the present inventors discovered genetic markers including KLRK1 region, individual HLA complex genes, cytokine genes, HMOX1 and other iron regulatory genes as predictors in sex-specific prenatal selection.

Example 10 Prediction of Propensity to Prenatal Loss: Individual SNP Analysis

Individually, RXRB rs421446, RXRB rs2076310, BRD2 rs635688, GTF2H4 rs3909130, HIST1H1T rs198844, SLC11A2 rs422982, SLC40A1 rs1439814, RRM2 rs1130609, IFNG rs2069727 showed frequency differences between males and females for wild-type allele or variant allele positivity (dominant genetic model). This is interpreted as being positive for a certain allele of these SNPs was unfavorable for male offspring and they were less likely to reach the end of pregnancy. As the odds ratios lie between 0.37 and 0.67, male offspring bearing any of these genotypes have at least 33% reduced chance of surviving pregnancy.

As will be seen in Table 3, some genotypes did not reach statistical significance using the conventional criterion (P≦0.05) but they are still listed if the association was marginally significant (P=0.06 to 0.10) and odds ratios were 0.67 or smaller. This was only done to be able to assess those SNPs in the multivariable models (in which they may reach statistical significance because of their small odds ratios).

Two RXRB SNPs reached statistical significance in their association with prenatal loss. When this happens, it is customary to examine their independence from each other because most common reason for this is that the two SNPs are correlated. In genetic data analysis, this means they are in linkage disequilibrium (LD). An examination of LD between RXRB SNPs rs421446 and rs2076310 showed extremely significant correlation (correlation coefficient=0.81, P<10⁻¹⁰). Multivariable modeling showed that the primary association was with rs2076310 and the other SNP showed an association simply because of its LD with rs2076310. Consequently, only RXRB rs2076310 was considered for further analysis in the next step (multivariable modeling).

TMPRSS6 rs733655, HMOX1 rs2071748 and IL-6 rs1800796 also showed individual associations with male-specific prenatal loss but with homozygous genotypes (homozygosity for the variant allele or the wild-type allele as indicated in Table 3). The odds ratios for these SNPs were between 0.38 and 0.61.

Finally, HLA-E rs1264456 also showed reduced frequency in males for its heterozygosity rate. This association had a borderline statistical significance (P=0.05) and an odds ratio of 0.67. This association represented a deleterious effect of heterozygosity for male offspring, which can be translated into heterozygous advantage for female offspring. All genotypes that showed associations with heterozygosity are discussed below.

The other SNPs listed there did not show any individual association but in combinations they were markers for male-specific prenatal loss. The three SNPs from the HLA complex (HLA-DQA1 rs1142316, HLA-DRA rs7192 and HSPA1B rs1061581) characterize the major HLA complex genetic lineages as first described by Dorak et al. (2006). In the present study, combined homozygosity for ancestral lineages showed a decreased frequency in male newborns compared with the homozygosity rate in female newborns (5.9% in males vs 14.6%, P=0.006, OR=0.36, 95% CI=0.18 to 0.75). The combinations that gave rise to this strong association are homozygosity for wild-type alleles in all three SNPs and homozygosity for variant allele in all three SNPs. Because of its strength, this association remained statistically significant in the multivariable model for prenatal loss as presented below (and in Table 4).

Table 4 lists the nine genotypes identified as independent markers for survival of a male offspring. Seven of those are individual SNP genotypes and two are particular genotype combinations of three SNPs, one in the HLA complex (HLA-DQA1-DRA-HSPA1B) and another in the KLRK1 region. The frequencies in male and female newborns as well as resulting odds ratios and P values are presented.

Likewise, heterozygosity at two IRF4 SNPs rs12203592 and rs872071 did not show an association individually but in combination (i.e., heterozygosity at both SNPs) (10.1% in males vs 19.4% in females, P=0.01, OR=0.47, 95% CI=0.26 to 0.85). This IRF4 combined genotype marker was included in the multivariable model for assessment of its independence but did not remain statistically significant.

Three KLRK1 (NKG2D) region SNPs listed at the end of Table 3 also showed statistically significant or marginally significant associations with odds ratios between 0.60 and 0.69. Since they were from the same gene region (KLRK1), their genotypes were combined to be used as a single marker. The combination included wild-type allele positivity for rs10772266 and heterozygosity for both rs2617160 and rs2617171 (21.7% in males vs 33.3% in females, P=0.01, OR=0.56, 95% CI=0.35 to 0.88). This KLRK1 region combined genotype remained statistically significant as an independent marker in the multivariable model.

Example 11 Multivariable SNP Analysis and Generation of Final Predictive Model

The outcome of pregnancy is not determined by a single genotype and our single marker analysis revealed multiple statistically significant associations. We therefore proceeded to the next step and analyzed the statistically significant associations by multivariable modeling to identify the most informative minimal subset of markers. These would be the statistically most significant and independent associations. Independence is important to avoid redundancy in testing samples and also for contributions to the additive model. Markers that are correlated and therefore not independent do not add to the information obtained from one of them and does not change the odds ratio when included in the multivariable final model.

The multivariable modeling yielded the independence and statistical significance of the nine markers listed in Table 4. The frequencies for each SNP in male and female newborns are replicated from Table 3 and the frequencies for two of the combined genotypes that remained statistically significant in the multivariable model are given in Table 4. In this final model, all but one adjusted odds ratios were smaller than 0.50 and therefore associated with less than 50% likelihood of a male offspring to reach the end of pregnancy.

Next, we assessed the value of this subset of markers in predicting the prenatal loss jointly. Since all associations were arranged to be in the same direction, it was possible to examine the additive effect of the sum of markers without any further manipulation. Each individual was simply given a score for the number of markers possessed. In the newborn group examined, there was no newborn who lacks all of the markers (score=0) or having all of them (score=9). Thus, the scores were between 1 and 8. The newborns were stratified into three groups: the baseline group consisted of newborns possessing any 1 to 3 of the nine markers (n=176), the next group consisted of 141 newborns who possessed any 4 of the nine markers and the third group was 96 newborns positive for any 5 or more of the nine markers.

Examination of the additive effect of these nine SNPs revealed a stepwise decrease in odds ratio corresponding decreasing likelihood of survival for male offspring as the number of markers possessed increases. The overall model reached extreme statistical significance (P<10⁻¹⁰). This was because, in reference to the baseline group of newborns possessing 1 to 3 of the markers, having 4 markers was associated with male-specific prenatal loss with an odds ratio of 0.37 (95% CI=0.23 to 0.59; P=0.00001) and again in reference to the baseline group, having 5 or more of the markers was associated with prenatal loss even more strongly (OR=0.20, 95% CI=0.11 to 0.34; P<0.00001). In other words, these figures translate into three-times decreased chance of survival for boys possessing 4 of the nine markers and five-times decreased chance of survival for boys possessing 5 or more compared with boys possessing 3 or fewer markers.

It is important that if such a model is useful in clinical use, the markers should occur at appreciable frequencies in the population. Frequencies of individual markers in newborn males and females are given in Tables 3 and 4. Table 3 shows the results of the analysis of 24 SNPs in newborns. The frequencies for the genotypes shown in male and female newborns, and their statistical evaluation as odds ratio, its 95% confidence interval and P value are shown.

In the cumulative risk model, possession of 4 markers occurred in 38.8% of female newborns, and possession of 5 or more markers in 32.7% of them. It is expected that at the beginning of pregnancy before selection occurs, males have similar frequencies (although at the end of pregnancy, these frequencies are 29.1% and 13.1%, respectively). Thus, at the beginning or at early phases of pregnancy, the risk markers will be present at a considerable frequency in offspring to allow risk stratification.

Experimental Protocols I. Characterization of Clinical Samples

The population sample analyzed in this study consisted of anonymously collected cord blood samples from newborns in South Wales (United Kingdom). Random, anonymous umbilical cord blood samples were obtained from full-term babies born in the University Hospital of Wales and Llandough Hospital in Cardiff, UK over a period of 12 months from 1996. This practice of collection of surplus biological material for research purposes anonymously was in compliance with the regulations of the local institutional ethics committee.

It was not practically possible to obtain samples from every newborn over this period but no newborn was intentionally excluded on the basis of any selection criteria. The samples were collected until the number in both sex groups exceeded 200. In the final group of 415 newborns, there were 201 boys and 214 girls. This gives a male-to-female (M:F) ratio of 0.939 that is slightly lower than the expected M:F ratio (1.056) in newborns (statistically non-significant).

These samples were previously used to describe the first marker for sex-specific prenatal loss. In the present study, 388 of the originally collected 415 samples were genotyped due to limited DNA availability (201 girls and 187 boys). No data are available about the newborns (such as gestational age, birth order, birth weight, parental age) other than their sex and that they were born via natural vaginal birth. No newborn born via cesarean section was included.

II. Genotyping Procedures

A) Allelic Discrimination Assays

TaqMan allelic discrimination assay utilizes an oligonucleotide probe labeled with a fluorescent reporter dye at the 5′ end of the probe and a quencher dye at the 3′ end of the probe. The proximity of the quencher to the reporter in the intact probe maintains a reduced fluorescence for the reporter. During the PCR reaction, the 5′ nuclease activity of DNA polymerase cleaves the probe, thereby separating the reporter dye and the quencher dye and resulting in increased fluorescence of the reporter. Accumulation of PCR product is detected directly by monitoring the increase in fluorescence of the reporter dye. The 5′ nuclease activity of DNA polymerase cleaves the probe between the reporter and the quencher only if the probe hybridizes to the target and is amplified during PCR. The probe is designed to straddle a target SNP position and hybridize to the nucleic acid molecule only if a particular SNP allele is present.

TaqMan allelic discrimination assays were performed on Stratagene MX3000P instruments. The standard thermal profile protocol was used with the modification of 90 seconds at 60° C. for 50 cycles. TaqMan® SNP genotyping assays purchased from ABI as 40× were diluted to 20× by adding Tris-HCl and EDTA at pH 8.0. 96-well plates were set up by adding 1.5 μl DNA (10 ng/μl), 4.625 μl ddH₂O and 6.25 μl TaqMan® genotyping master mix (ABI) and 0.625 μl assay reagents. Each plate contained intra and inter-plate controls and no-template controls. Built-in Stratagene Mx3000P software was used to assign genotypes.

B) Polymerase Chain Reaction—Restriction Fragment Length Polymorphism (PCR-RFLP) Analysis

In these series of study, PCR-RFLP analysis was performed to genotype the HSPA1B SNP rs1061581. In this analysis, oligonucleotides 5′-CAT CGA CTT CTA CAC GTC CA-3′ (SEQ ID NO: 1) and 5′-CAA AGT CCT TGA GTC CCA AC-3′ (SEQ ID NO: 2) and the restriction endonuclease PstI were used. In the first step, using the oligonucleotides, a 1,117 bp fragment was amplified by PCR. The fragments were then subjected to restriction endonuclease digestion by using the PstI enzyme. This enzyme cuts the fragment into two fragments of 934 bp and 183 bp when there is a nucleotide G in the SNP position but fails to cut it when there is a nucleotide A in the SNP position. Samples with only 934 bp and 183 bp fragments were classified as homozygote for allele G and samples with only the 1,117 bp fragment were classified as homozygote for allele A. Samples that contained 1,117 bp, 934 bp and 183 bp fragments were classified as heterozygote for alleles A and G.

PCR-RFLP analysis was performed to genotype the HLA-DQA1 3′UTR SNP rs1142316. In this analysis, oligonucleotides 5′-CAA GGG CCA TTG TGA ATC YCC AT-3′ (SEQ ID NO: 3) and 5′-TGG GYG GCA RTG CCA A-3′(SEQ ID NO: 4) and the restriction endonuclease BglII were used. In the first step, using the oligonucleotides, a 726 bp fragment was amplified by PCR. PCR was done under standard conditions using 20 ng of genomic DNA and annealing temperature of 57° C. The fragments were then subjected to restriction endonuclease digestion by using the BglII enzyme. This enzyme cuts the fragment into two fragments of 513 bp and 213 bp when there is a nucleotide C in the SNP position but fails to cut it when there is a nucleotide A in the SNP position. Samples with only 513 bp and 213 bp fragments were classified as homozygote for allele C and samples with only the 726 bp fragment were classified as homozygote for allele A. Samples that contained 726 bp, 513 bp and 213 bp fragments were classified as heterozygote for alleles A and C.

PCR-RFLP analysis was also used to genotype MDM2 SNP rs2279744. For the MspA1I RFLP analysis, primers 5′-CGG GAG TTC AGG GTA AAG GT-3′ (SEQ ID NO: 5) and oligonucleotide 5′-AGC AAG TCG GTG CTT ACC TG-3′ (SEQ ID NO: 6) were used. PCR was done under standard conditions using 20 ng of genomic DNA and annealing temperature of 66° C. The resulting PCR product (351 bp) was digested by MspA1I. MspA1I cleaves final PCR product on two sites, one is constitutive that served as an internal control of enzymatic digestion and allele G of SNP309 generates specific MspA1I restriction site.

Table 5 shows the flanking DNA sequence of each SNP. The SNPs are shown as the wild-type and variant alleles. Table 6 lists the different genotyping methods used to genotype SNPs analyzed in this invention.

III. Statistical Analysis

The statistical analysis of a SNP association may be performed using the following statistical models. It may be of importance to have the variant allele in homozygous or heterozygous combination as long as there is at least one copy of it in the genotype (CT and TT). In this case, individuals with CT or TT genotypes are pooled together and coded as 1 in a variable that are going to be used in the statistical analysis. The code 1 indicates presence of the susceptibility marker. In this case, individuals who have the homozygous wild-type genotype are coded as 0 meaning the lack of the susceptibility marker. This model that pools heterozygotes and homozygotes together is called dominant genetic model.

In recessive model, the interest in on homozygous genotype of the variant allele (TT) and individuals with the TT genotype are coded as 1 while all other genotypes are coded as 0. There are certain situations in which the number of variant allele possessed is important because having 1 or 2 copies of the variant allele correlates with the degree of susceptibility. In this case, individuals with genotype CT (one copy of the variant allele) have increased susceptibility and individuals with genotype TT (two copies of the variant allele) have an even higher degree of susceptibility. This model is called the additive model and demonstrates a gene-dosage effect. In most cases, statistical significance for this model is usually an indication of an association with dominant or recessive model. In our analysis that follows, we have presented dominant or recessive model associations for each SNP. Variables with P values of less than 0.05 were considered statistically significant. Statistical association analysis was carried out using logistic regression with Stata version 10 statistical software.

One exceptional situation is that the heterozygous genotype CT may be of importance. Heterozygosity in the genome is shown to be a beneficial trait for prevention from many common diseases including infections and cancer. This situation is called ‘heterozygote advantage’ and is characterized by decreased frequency or underrepresentation of a heterozygous genotype among cases with a disease compared with normal controls because of its protective effect from the condition. In prenatal selection, heterozygote advantage confers survival benefit is observed at higher frequency. The dependency of the HLA complex-mediated heterozygote advantage on sex in prenatal selection has already been reported.

As mentioned above, each individual is coded as 0 or 1 based on the absence or presence of the susceptibility genotype(s) for each SNP before statistical association. A SNP may have a deleterious or beneficial effect on a condition. In the present invention, the outcome of interest was sex-specific prenatal survival. In this case, beneficial genotypes are overrepresented in the favored sex and deleterious genotypes for a particular sex are underrepresented in that sex group among newborns who have survived the selection. To avoid intricate mathematical manipulations while constructing a statistical model to find the most informative subset of SNPs, it is desirable that all SNPs are beneficial or deleterious. This means, it is easier to construct a model if the direction of the effect is the same for each SNP. In the case of SNP associations, this is achieved easily. Since each individual is coded as 0 or 1, when necessary, an association that is beneficial for one sex can be converted to a deleterious one by simply reversing the statistical codes. Males are under greater selective pressure during pregnancy and our aim was to find deleterious genotypes for males. We were interested in genotypes that were underrepresented in male newborns compared with female newborns. All results presented here are in this direction and the genotypes that give rise to an association in that direction (i.e., deleterious for males) are given in the text and tables. In terms of the odds ratio, which is a measure of the strength of association, they are all less than 1.0. The odds ratio approximates the survival chance of a male fetus to the end of pregnancy. Thus, a value of 0.49 suggests, a male conceptus with this genotype has a survival probability of 49% as opposed to 100% for the comparison group who are female newborns.

All patents, publications, accession numbers, and patent application described supra in the present application are hereby incorporated by reference in their entirety.

Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to those of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

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TABLE 1 List of Genes and SNP Evaluated for Their Predictive Value as Markers for Prenatal Loss Chromosome Genes and SNP Position SNP ID position HFE2 (HJV)-5′FLANK rs4970862 chr1: 144132834 HFE2 (HJV)-3′FLANK rs16827043 chr1: 144106797 IL10 rs1800896 chr1: 205013520 PKR (EIF2AK2)-IVS2 rs2270414 chr2: 37216952 PKR (EIF2AK2)-IVS1 rs12712526 chr2: 37224339 PKR (EIF2AK2)-5′UTR rs2254958 chr2: 37229795 RRM2-5′UTR rs1130609 chr2: 10180371 STEAP3-5′UTR rs1562256 chr2: 119687643 STEAP3-ivs1 rs865688 chr2: 119699720 STEAP3-IVS1 rs865108 Chr2: 119702854 CYBRD1-IVS1 rs960748 chr2: 172088182 CYBRD1-IVS1 rs6759240 chr2: 172089044 CYBRD1-ex4 (G266A) rs10455 chr2: 172119519 SLC40A1(V221V) rs2304704 chr2: 190138422 SLC40A1-IVS2 rs1439812 chr2: 190148793 SLC40A1-IVS2 rs1439814 chr2: 190151138 SLC40A1-IVS7 rs1439816 chr2: 190152875 SLC11A1/NRAMP1-IVS4 rs3731865 chr2: 218958247 SLC11A1/NRAMP1-5′UTR rs1059823 chr2: 218968088 TF-P589S (Ex 15) rs1049296 chr3: 134977044 TF-L524L (Ex 13) rs8649 chr3: 134969648 TF-5′ UTR rs1130459 chr3: 134947973 TF-5′ FLANK rs4481157 chr3: 134947374 TF-5′ FLANK rs16840812 chr3: 134945497 CP-E543D (Ex 9) rs701753 chr3: 150398925 CP-IVS1 rs7652826 chr3: 150421640 TFRC-S142G (Ex 4) rs3817672 chr3: 197285208 TFRC (5′UTR) rs11915082 chr3: 197293536 NFKB1-IVS6 rs4648022 chr4: 103715475 DAXX-IVS1 rs2073524 chr6: 33398525 DAXX-Y379Y (ex4) rs1059231 33396249 DAXX-IVS4 rs2239839 33396053 RXRB-5′FLANK rs421446 33282761 RXRB-5′FLANK rs365339 33280883 RXRB-IVS3 rs2076310 33274012 RXRB-F384F rs6531 33271429 BRD2-3′UTR rs1049414 33056585 BRD2-IVS7 rs11908 33052724 BRD2-IVS3 rs635688 33051129 BRD2-5′FLANK rs206786 33043157 TAP2 rs241453 32904204 HLA-DQB2 rs1573649 32839236 HLA-DQA2 rs2227128 32819378 HLA-DQA1-3′UTR rs1142316 32686523 HLA-DQA1-IVS2 rs9272723 32717405 HLA-DQA1-IVS1 rs17426593 32716055 HLA-DRB1 to DQA1 rs17599077 32699036 HLA-DRB1 to DQA1 rs3129763 32698903 HLA-DRB1 to DQA1 rs9271586 32698877 HLA-DRB1 to DQA1 rs2395225 32698602 HLA-DRB1 to DQA1 rs3135005 32693997 DRA-3′FLANK rs3135388 32521029 DRA-3′UTR rs7194 32520458 DRA-L242V (exon 4) rs7192 32519624 DRA-I134I (exon 3) rs8084 32519013 DRA-V16L (exon 1) rs16822586 32515751 DRA-5′UTR rs14004 32515687 BTNL2 (Q350Q) rs9268480 32471822 BTNL2 rs2076530 chr6: 32471794 BTNL2 rs3129953 32469799 C6orf10 rs9268428 32452951 C6orf10-IVS6 rs1265758 32431507 NOTCH4-5′FLANK rs3096690 32302608 NOTCH4-5′FLANK rs3096702 32300309 NOTCH1-IVS1 rs396960 32299559 NOTCH4-K117Q (exon 3) rs915894 32298368 NOTCH4-S244L (exon 4) rs8192585 32296801 NOTCH4-IVS11 rs3134799 32292199 PBX2-IVS4 rs204993 32263559 PBX2-3′FLANK rs1800684 32259972 EGFL8-3′UTR rs1061808 32244525 EGFL8-R86K rs3096697 32242488 TNXB-3′UTR rs8283 32191278 TNXB-3′FLANK rs3130342 32188124 TNXB-H1248R rs185819 32158045 CYP21A2-V282L rs6471 32115866 CYP21A2-R103K rs6474 32114865 SKIV2L-Y1067Y (exon 26) rs410851 32044647 SKIV2L-IVS6 rs419788 32036778 SKIV2L-IVS6 rs2280774 32036670 SKIV2L Ex5 Q151R rs438999 32036285 SKIV2L-IVS2 rs440454 32035321 CFB-IVS14 rs1270942 32026839 CBF-R32W rs12614 32022158 HSPA1B-Q351Q rs1061581 31904759 HSPA1B-5′FLANK (−1136) rs2763979 31902571 HSPA1A-5′UTR (−27G > C) rs1043618 31891486 HSPA1L-T493M rs2227956 chr6: 31886251 HSPA1L-G602K rs2075800 31885925 MSH5 rs1802127 31837904 MSH5-Q716Q rs707938 31837338 MSH5 rs3131378 31833264 MSH5 rs707939 31834667 MSH5 rs28381349 31817024 MSH5 rs2075789 31816307 CLIC1 rs3131383 31812273 CLICI rs2272592 31806331 BAT3-IVS6 rs805303 31724345 BAT3-IVS12 rs2077102 31719819 BAT3-3′FLANK rs2736155 31713178 AIF1-IVS4/R15W rs2269475 31691910 AIF1-5′UTR/IVS3 rs2259571 31691806 AIF1-IVS1 rs2844475 31691134 NCR3-5′UTR rs986475 31664688 NCR3-3′UTR rs1052248 31664560 NCR3-3′FLANK rs2256965 31663109 TNF (promoter-238) rs361525 31651080 TNF-5′FLANK (promoter-857) rs1799724 31650461 LTA-IVS1 rs909253 31648292 NFKBIL1 rs2071592 31623319 MICA-V152M rs1051792 31486956 HLA-C-5′FLANK rs9264942 31382359 POU5F1-IVS1 (Ex1 − M1R) rs3130932 31241922 POU5F1-IVS4 rs2394882 31240628 TCF19-P219P rs2073722 31237621 TCF19-IVS1 rs6905862 31235581 TCF19-IVS1 rs1150765 chr6: 31235541 GTF2H4-IVS11 rs1264307 30988736 GTF2H4 rs1264309 30983878 GTF2H4-5′FLANK rs3909130 30982144 DDR1 rs1049623 30972808 DDR1 rs1264323 30963886 DDR1 rs1264327 30958561 DDR1 rs1264328 30958121 IER3-3′UTR rs10947089 30818114 HLA-E-3′FLANK rs1264456 30570063 HLA-E-5′FLANK rs1264459 30563899 ZNRD1 rs9261269 30138093 HLA-G-3′UTR rs1704 29906560 HLA-G-5′FLANK rs1736939 29901364 UBD (5′FLANK) rs1233405 29637733 UBD (IVS1) rs2534790 29632147 UBD (Ex2-T68C) rs2076485 29631931 UBD (Ex2-C160S) rs8337 29631655 HIST1H1T-V14L rs198844 26216261 HIST1H4C-5′FLANK rs198853 26212075 HIST1H4C-5′ & HFE-3′FLANK rs17596719 26205173 HIST1H4C-5′ & HFE-3′FLANK rs12346 26205025 HFE-3′FLANK rs707889 26203910 HFE-IVS5 rs2858996 26202005 HFE-C282Y rs1800562 26201120 HFE-H63D rs1799945 26199158 HFE-S65C rs1800730 26199164 HFE-IVS2 rs2071303 26199315 HFE-IVS1 rs9366637 26197077 HFE-5′FLANK rs2794719 chr6: 26196869 HFE-5′FLANK rs2794720 26195181 HFE-5′FLANK rs1800702 26194442 HFE-5′FLANK rs4529296 26191114 HFE-HIST1H1C intergenic rs2050947 26178058 HIST1H1C-5′FLANK rs807212 26173600 HIST1H1C-5′FLANK rs9358903 26169928 HIST1H1C 5′FLANK rs9393682 26165029 HIST1H1C-S36S rs10425 26164528 HIST1H1C-P195P rs8384 26164051 HIST11H2AB-L97L rs2230655 26141485 HIST1H3B (3′UTR) rs2213284 26139847 HIST1H4A (5′FLANK) rs9467664 26129792 SLC17A3 rs1165165 25970445 PRL (promoter) rs1341239 22412183 CDKAL1 rs6908425 20836710 Ch6: 20099022 rs965036 20099022 EDN1-3′FLANK rs4714384 12405839 EDN1-3′FLANK rs4714383 12405468 EDN1 (K198N − Ex5) rs5370 12404241 EDN1-IVS4 rs1626492 12403489 EDN1-IVS2 rs1476046 12401207 EDN1-5′FLANK rs3756863 12397016 Ch6: 9559183 rs10484246 9559183 IRF4 rs4985288 327246 IRF4 rs9405192 327537 IRF4-5′FLANK rs1033180 328546 IRF4-IVS4 rs12203592 341321 IRF4 rs3778607 348799 IRF4 rs2001508 chr6: 349632 IRF4 rs7768807 353246 IRF4 rs1877175 355493 IRF4-3′UTR rs9392502 355608 IRF4-3′UTR rs872071 356064 IRF4 rs11242865 356954 IRF4 rs7757906 357741 IRF4 rs9378805 362727 IGFBP3-5′FLANK rs2854744 chr7: 45927600 TFR2-IVS17 rs10247962 chr7: 100057865 TFR2-IVS3 rs7385804 chr7: 10073906 TFR2-5′FLANK rs4434553 chr7: 10078127 SLC39A14-5′FLANK rs4872476 chr8: 22266179 SLC39A14-5′FLANK rs11136002 chr8: 22273027 SLC39A14-L33C rs896378 Chr8: 22318266 SLC39A14-IVS8 rs10101909 chr8: 22332985 H19 rs217727 chr11: 1973484 RRM1-IVS2 rs232054 chr11: 4680003 KLRK1 3′ rs10772266 chr12: 10397436 KLRK1 3′ rs1049174 chr12: 10416632 KLRK1 (intron 1) rs2617160 chr12: 10436864 KLRK1 (intron 1) rs2246809 chr12: 10448311 KLRC4 (intron 3) rs2734565 chr12: 10451858 KLRC4 S104N (ex3) rs2617170 chr12: 10452224 KLRC4 (intron 2) rs2617171 chr12: 10452546 KLRC4 S29I (ex1) rs1841958 chr12: 10453356 KLRC1-5′FLANK rs1983526 chr12: 10499280 KLRC1-5′FLANK rs2900421 chr12: 10513314 SLC11A2 (NRAMP2)-IVS4 rs224589 chr12: 49685317 SLC11A2 (NRAMP2)-IVS1 rs422982 chr12: 49692621 SLC11A2 (NRAMP2)-IVS1 rs224575 chr12: 49705888 IFNG-3′ FLANK rs2069727 chr12: 66834490 MDM2-IVS1 (SNP309) rs2279744 chr12: 67488847 IGF1 Exon 4-3′UTR rs6220 chr12: 101318645 IGF1-IVS3 rs1520220 chr12: 101320652 IREB2 rs2656070 chr15: 76517307 IGFIR-Exon 16-E1043E rs2229765 chr15: 97295748 HP_5′UTR rs9924964 chr16: 70643062 HP_5′UTR rs7203426 Chr16: 70644056 HP_IVS1 rs2070937 chr16: 70647241 TP53_Ex4 R72P rs1042522 chr17: 7520197 BRIP1-IVS4 rs4968451 chr17: 57282089 HAMP-5′FLANK rs1882694 chr19: 40463222 HAMP-5′FLANK rs10414846 chr19: 40464311 HAMP-IVS1 rs8101606 ch19: 40466396 HAMP-IVS1 rs7251432 chr19: 40467281 BMP2-3′FLANK rs235756 chr20: 6715111 LIF-3′UTR rs929271 chr22: 28968226 LIF-IVS2 rs737921 chr22: 28970214 LIF-IVS2 rs929273 chr22: 28970595 LIF-5′FLANK rs2267153 chr22: 28973609 LIF-5′FLANK rs3761427 chr22: 28974826 LIF-5′FLANK rs9606708 chr22: 28976126 HMOX1-IVS1 rs2071748 chr22: 34107618 HMOX1-IVS2 rs9607267 chr22: 34111207 HMOX1-IVS3 rs2071749 chr22: 34113413 HMOX1-3′UTR rs743811 chr22: 34122974 TMPRSS6-Y739Y rs2235321 chr22: 35792872 TMPRSS6-V736A (Ex17) rs855791 chr22: 35792882 TMPRSS6-D511D (Ex13) rs4820268 chr22: 35799537 TMPRSS6-IVS2 rs733655 chr22: 35824997 TMPRSS6-5′UTR rs5756515 chr22: 35829638 HEPH-5′FLANK rs5919015 X chr: 65299410 HEPH-5′UTR rs1028348 X chr: 65300888 HEPH-IVS7 rs760866 X chr: 65330706 HEPH-Exon 13 (Y498Y) rs806607 X chr: 65343765 HEPH-Exon 13 (T526T) rs809363 X chr: 65343849 HEPH-IVS14 rs708966 X chr: 65370647 HEPH-IVS18 rs4827365 X chr: 65397067 HEPH-IVS18 rs2198868 X chr: 65399577

TABLE 2 Characteristics of Single Nucleotide Polymorphisms and Other Polymorphisms Found To Be Predictors of Prenatal Loss in Univariable Statistical Association Tests Position in Genes SNP name Alternative Name Gene/Change RXRB rs421446 NT_007592.14: g.24033033A > G 5′ flanking region, T > C RXRB rs2076310 NT_007592.14: g.24024284A > G intron 3, T > C BRD2 rs635688 NT_007592.14: g.23801401T > C intron 3, C > T HLA-DQA1 rs1142316 no alternative name 3′UTR, A > C HLA-DRA rs7192 NT_007592.14: g.23269895T > G exon 4, G > T (L242V) HSPA1B rs1061581 no alternative name exon 1, A > G (Q351Q) GTF2H4 rs3909130 NT_007592.14: g.21732416A > G 5′ flanking region, C > T HLA-E rs1264456 no alternative name 3′ flanking region, C > T HIST1H1T rs198844 NT_007592.14: g.16966532C > G exon 1, C > G (L14V) IRF4 rs12203592 NT_034880.3: g.336321C > T intron 4, C > T IRF4 rs872071 NT_034880.3: g.351064A > G 3′UTR, G > A LIF rs929271 NT_011520.11: g.10028795T > G 3′UTR, T > G TP53 rs1042522 NT_010718.15: g.7176820G > C exon 4, C > G (R72P) MDM2 rs2279744 NT_029419.11: g.31345886T > G intron 1, T > G (SNP309) SLC11A2 rs422982 NT_029419.11: g.13549660T > A intron 1, T > A (NRAMP2) SLC40A1 rs1439814 NT_005403.16: g.40652310C > T intron 2, T > C RRM2 rs1130609 NT_005334.15: g.5097055T > G 5′UTR, G > T TMPRSS6 rs733655 NT_011520.11: g.16885566T > C intron 2, T > C HMOX1 rs2071748 NT_011520.11: g.15168187G > A intron 1, G > A IFNG rs2069727 NT_029419.11: g.30691529T > C 3′ flanking region, A > G IL6 rs1800796 NT_007819.16: g.22255204G > C promoter, G > C KLRK1 region rs10772266 no alternative name intergenic KLRK1 region rs2617160 NT_009714.16: g.3304571A > T intron 1, A > T KLRK1 region rs2617171 NT_009714.16: g.3320253C > G intron 2, C > G

TABLE 3 Individual Predictive Value of the Single Nucleotide Polymorphisms and Other Polymorphisms or Their Combinations Frequency in Univariable Odds Males vs Ratio (95% CI) Genes/SNP/Genotypes Females (%) and P value RXRB rs421446/ 45.2 vs 55.3 OR = 0.66 variant allele positivity (0.44 to 0.99), P = 0.05 RXRB rs2076310/ 35.7 vs 49.0 OR = 0.58 variant allele positivity (0.38 to 0.87), P = 0.009 BRD2 rs635688/ 71.5 vs 80.2 OR = 0.62 wildtype allele positivity (0.38 to 0.99), P = 0.05 HLA-DQA1 rs1142316*/ 58.7 vs 59.1 OR = 0.99 combined homozygous genotypes (0.64 to 1.50), P = 0.94 HLA-DRA rs7192*/combined 52.4 vs 58.7 OR = 0.78 homozygous genotypes (0.52 to 1.16), P = 0.22 HSPA1B rs1061581*/combined 50.0 vs 54.0 OR = 1.17 homozygous genotypes (0.80 to 1.73), P = 0.42 GTF2H4 rs3909130/ 89.0 vs 95.7 OR = 0.37 wildtype allele positivity (0.16 to 0.82), P = 0.02 HLA-E rs1264456/ 37.6 vs 47.3 OR = 0.67 heterozygosity (0.45 to 1.00), P = 0.05 HIST1H1T rs198844/ 18.1 vs 28.9 OR = 0.54 variant allele positivity (0.33 to 0.89), P = 0.02 IRF4 rs12203592/ 35.7 vs 32.7 OR = 0.73 heterozygosity (0.48 to 1.13), P = 0.16 IRF4 rs872071/ 45.5 vs 52.9 OR = 0.74 heterozygosity (0.49 to 1.12), P = 0.16 LIF rs929271**/ 42.4 vs 51.0 OR = 0.71 wild-type homozygosity (0.47 to 1.07), P = 0.10 TP53 rs1042522**/ 55.0 vs 58.0 OR = 0.88 wild-type homozygosity (0.59 to 1.31), P = 0.54 MDM2 rs2279744**/ 45.2 vs 45.2 OR = 1.00 wild-type homozygosity (0.67 to 1.47), P = 0.99 SLC11A2 rs422982/ 40.3 vs 50.2 OR = 0.67 variant allele positivity (0.45 to 1.0), P = 0.05 SLC40A1 rs1439814/ 57.4 vs 67.5 OR = 0.65 variant allele positivity (0.43 to 0.98), P = 0.04 RRM2 rs1130609/ 38.2 vs 48.1 OR = 0.66 variant allele positivity (0.44 to 1.01), P = 0.06 TMPRSS6 rs733655/ 2.03 vs 5.16 OR = 0.38 variant allele homozygosity (0.12 to 1.22), P = 0.10 HMOX1 rs2071748/ 14.7 vs 22.1 OR = 0.61 variant allele homozygosity (0.36 to 1.02), P = 0.06 IFNG rs2069727/ 80.1 vs 87.7 OR = 0.56 wild-type allele positivity (0.33 to 0.97), P = 0.04 IL6 rs1800796/ 81.4 vs 88.0 OR = 0.60 wildtype homozygosity (0.34 to 1.04), P = 0.07 KLRK1 rs10772266***/ 70.9 vs 80.1 OR = 0.60 wild-type allele positivity (0.38 to 0.97), P = 0.04 KLRK1 rs2617160***/ 34.8 vs 43.6 OR = 0.69 heterozygosity (0.46 to 1.05), P = 0.08 KLRK1 rs2617171***/ 35.9 vs 44.6 OR = 0.69 heterozygosity (0.46 to 1.05), P = 0.08 *These SNPs make up the HLA-DQA1-DRA-HSPA1B haplotype. Individually they have no effect on prenatal loss. **These SNPs do not show any effect on prenatal loss but in interaction with MDM2 and TP53 SNPs, the LIF SNP influences viability of male offspring. ***Individually, these SNPs do not show any effect individually but in combination of the genotypes shown, they are a combined KLRK1 marker for loss of male offspring before birth.

TABLE 4 Single nucleotide polymorphisms and other polymorphisms found to be independent predictors of prenatal loss in multivariable statistical modeling Frequency in Adjusted odds ratio Gene/SNP/Genotype Males vs Females (%) (95% CI) and P value RXRB rs2076310/ 35.7 vs 49.0 OR = 0.45 variant allele positive (0.27 to 0.74), P = 0.002 HLA-DQA1 rs1142316/ 5.85 vs 14.6 OR = 0.31 homozygosity (0.13 to 0.77), HLA-DRA rs7192/ P = 0.01 homozygosity HSPA1B rs1061581/ homozygosity GTF2H4 rs3909130/ 89.0 vs 95.7 OR = 0.28 wildtype allele positive (0.09 to 0.81), P = 0.02 HIST1H1T rs198844/ 18.1 vs 28.9 OR = 0.47 variant allele positive (0.26 to 0.85), P = 0.01 IFNG rs2069727/ 80.1 vs 87.7 OR = 0.47 wildtype allele positive (0.24 to 0.91), P = 0.03 IL6 rs1800796/ 81.4 vs 88.0 OR = 0.38 wildtype homozygous (0.19 to 0.78), P = 0.008 KLRK1 rs10772266/ 21.7 vs 33.3 OR = 0.55 wildtype allele positive (0.32 to 0.96), KLRK1 rs2617160/ P = 0.035 heterozygous KLRK1 rs2617171/ heterozygous TMPRSS6 rs733655/ 2.03 vs 5.16 OR = 0.09 variant homozygous (0.01 to 0.78), P = 0.03 HMOX1 rs2071748/ 14.7 vs 22.1 OR = 0.47 variant homozygous (0.24 to 0.90), P = 0.02

TABLE 5  Single Nucleotide Polymorphisms Found to Predict Sex-Specific Prenatal Selection RXRB rs421446 C/T: GAGGGCCACC TGTTCCAAGA CCCCCTTTCA AGGCCAGACT GGACACCAAG ATGGGGCCAT GAACAAATCA CCCTTGGGGA CCATAAGAAC CCAGGGAGTT GGGGGGAGGG GACTGGTGCT GCAGAACCAG TGGAAAGGGG TGACGCACGA ACCCCTCCCT C/T CAAAAAGACC CGGAGTGTCA CGCATACACA GTGACACATA CTCTTTCCTC TCACACCCGG CGGCGGGGGT TGCCCTGGGA GACCAGGCAG AGAAAGGGAA CAATCCTTCG GGAAAGGGAA AGGAGGGGGA GGTGGGGAAG GGTCTGAGGG CTTGGACACA AGAAGAGCCG GAGGTGGCAG RXRB rs2076310 C/T: AGATGTGAAG CCACCAGTCT TAGGGGTCCG GGGCCTGCAC TGTCCACCCC CTCCAGGTGG CCCTGGGGCT GGCAAACGGC TATGTGCAAT CTGCGGGGAC AGAAGCTCAG GTATGTGGCT CAGAGGATGA ACAGAGAGGG AGAGTCTGGG CCATGTATCA C/T CACCTGTGGG ATTCCCAGGG CTTATGGAGT TTGGTCAGAG CAAGTGACCT GGGGGAGGCC TGATGGGAGT AAAGAAGCTG AAGCTGAGAT GTAGGACGCG ATTGGGGGGA AGGTCAGAGG GAAAAGGAAG CAGCGTGTAG GGTTTCTGAA CAGTGAGGAG ACTGGGACTG GATCATCACT BRD2 rs635688 C/T: ATTTATTTAT TTTGTCCCAC AGTTTAATTG GGGCCGCAGT TTAAGTAACT GTTCCTTTGA TGCATAGGGG GGGTGTGTGT GTGTGTGTGT GTGTGTGAGA GTCGGGGATC GGTAGTCTCC CTATAAGCAT TTATTTTTCT GTGGTTCTGA CCTAACATTT C/T TTTATTTAGG ATTATCACAA AATTATAAAA CAGCCTATGG ACATGGGTAC TATTAAGAGG AGACTTGAAA ACAATTATTA TTGGGCTGCT TCAGAGTGTA TGCAAGATTT TAATACCATG TTCACCAACT GTTACATTTA CAACAAGGTG AGTTTTTCTG TGTGTTCATT TAGTAGGTGG HLA-DQA1 rs1142316 A/C: TAACATCGAT CTAAAATCTC CATGGAAGCA ATAAATTCCC TTTAAGAGAT A/C TATGTCAAAT TTTTCCATCT TTCATCCAGG GCTGACTGAA ACCGTGGCTA HLA-DRA rs7192 G/T: CTTCTTCCCA CACTCATTAC CATGTACTCT GCCTTATTTC CCCCCAGAGT TTGATGCTCC AAGCCCTCTC CCAGAGACTA CAGAGAACGT GGTGTGTGCC CTGGGCCTGA CTGTGGGTCT GGTGGGCATC ATTATTGGGA CCATCTTCAT CATCAAGGGA G/T TGCGCAAAAG CAATGCAGCA GAACGCAGGG GGCCTCTGTA AGGCACATGG AGGTGAGTTA GGTGTGGTCA GAGGAAGACG TATATGGAGA TATCTGAGGG AGGAAAACAG GGTGGGGAAA GGAAATGTAA TGCATTTAAG AGACAAGGTA GGAACAGATG TGGCTCTTGA TTTCTCTTTG HSPA1B rs1061581 A/G: CCAGGGCGAG GTTCGAGGAG CTGTGCTCCG ACCTGTTCCG AAGCACCCTG GAGCCCGTGG AGAAGGCTCT GCGCGACGCC AAGCTGGACA AGGCCCAGAT TCACGACCTG GTCCTGGTCG GGGGCTCCAC CCGCATCCCC AAGGTGCAGA AGCTGCTGCA A/G GACTTCTTCA ACGGGCGCGA CCTGAACAAG AGCATCAACC CCGACGAGGC TGTGGCCTAC GGGGCGGCGG TGCAGGCGGC CATCCTGATG GGGGACAAGT CCGAGAACGT GCAGGACCTG CTGCTGCTGG ACGTGGCTCC CCTGTCGCTG GGGCTGGAGA CGGCCGGAGG CGTGATGACT GTF2H4 rs3909130 A/G: TTAAAATCTT CAAAGAACAG CTAAAAATTG ACAGAGCTTC TTTATGGCAA ACTTTAGGTA AGGTTGAAAG ACAATTTACA ATCTAGGAAG AAATGGTTGA TGAAATAAAC AAAATACAAA AAGCTGTTAC AAAGCAATAA GAAAAAGAAA CATAATAGAA A/G GATTGGGACA GACCACTGCT TACTAGTTAG CCCTGCTCAG CAAGGAGCAG CTTAAAAAAA AAAAAAGAAG AAGAAAAGAA AAAGAAAAGA AAGAGGCCTG GCGGGGTGGC TCAGGCCTGT AATCCCAACA CTTTGGGAGG CCAAAGAAGG TGGATCATTT TAGCTCAGGA GTTCCAGACC HLA-E rs1264456 C/T: CACAGGAAGA AATGGCAAAG TAAAAATTCA CACCCAGGAC TCCCTGGGCT TTCTCACCGC ACATGTTGCC TTCTTACTGG ATATCACCTG ACAGAATGAG ACTCAGGTGA TTACAGGGAT TCACCAGGAA AACGGGAAAG TCGGCATGAC CAGAACTAGA ACA C/T GGGCCAGTGA ATGCAGTTCT GGGTGGACCA TGGCATTGGA AGCCAAAGGA TAGCTTGAAT GTGGTTAAAA AATTAAAACA ACAAGGCACA AAACGCACAA ATGAAATACA AATGATGCTC AAACACAGCT TTTATTTTAC TTCAAAGTTT ACCTCAGATC AGCCTGGGAA GGTGAGGGGA HIST1H1T rs198844 C/G: GTGACACTGA AAGGGCCTCG GTGATCAACT TGGACACAGA GAGGTTCGGC ACTTTGCGAC TTGCACTTAT CAAGCCAGCC GGCTTCCTCC CTCGCTTCTT GGTTGGAAGT TTCTCCATAG CGGCTA C/G ACCAGCACTG GCAGAAGCTG CAGGCACGGT TTCAGACATA ACAACAGAGA AACGCAAGAT GTAATAACCA GCGAAAAGCA TGAAACACCC GGGCGGCCTC GGGGCCTTAT ATAGGGTAGG GCGCGCTGTG ATTGGTGCAT CACCTAGGCA CCGCCCCCGC CCCTTGGAGG AGGAGTATTT IRF4 rs12203592 C/T: ATGTTTTGTG GAAGTGGAAG ATTTTGGAAG TAGTGCCTTA TCATGTGAAA CCACAGGGCA GCTGATCTCT TCAGGCTTTC TTGATGTGAA TGACAGCTTT GTTTCATCCA CTTTGGTGGG TAAAAGAAGG C/T AAATTCCCCT GTGGTACTTT TGGTGCCAGG TTTAGCCATA TGACGAAGCT TTACATAAAA CAGTACAAGT ATCTCCATTG TCCTTTATGA TCCTCCATGA GTGTTTTCAC TTAGTCTGAT GAAGGGTTCA CTCCAGTCTT TTCGGATGAT AAAATGCTTC GGCTGTCAGT CTAATAAGGG IRF4 rs872071 A/G: TGTTTTACAT GCCCCGTTTT TGAGACTGAT CTCGATGCAG GTGGATCTCC TTGAGATCCT GATAGCCTGT TACAGGAATG AAGTAAAGGT CAGTTTTTTT TTGTATTGAT TTTCACAGCT TTGAGGAACA TGCATAAGAA ATGTAGCTGA AGTAGAGGGG A/G CGTGAGAGAA GGGCCAGGCC GGCAGGCCAA CCCTCCTCCA ATGGAAATTC CCGTGTTGCT TCAAACTGAG ACAGATGGGA CTTAACAGGC AATGGGGTCC ACTTCCCCCT CTTCAGCATC CCCCGTACCC CACTTTCTGC TGAAAGAACT GCCAGCAGGT AGGACCCCAG AGGCCCCCAA IFNG rs2069727 T/C: TGTGGTATTT CTTTCCACTA GCATTTTGTT GGCTTTCGCT TTTCCAGTTA GCAGCTCTTT GAATTATCTT TCTAAGATAC AGATTTAATT ATGTCACTAT TCAATTCAGA GGTTCTGCTA TGGAATGTAG TTTAAACTGC TTAGCTTGGC ACACAGAGAT TTATTTCTAG CCCCTTCTCC ACCTTCCTAT TTCCTCCTTC T/C TTTCAGAATC TTCCTCTCCC TCATCCAATG CTGGCAAACA CCAGTGGGGG TGGAGTAGTG GGTGTAAGCT CTAGGGAGAA GGCTTGGATT GGAATCCAAG TTATTCCATT ACAAGTAGTG TGACCTTTAA TACATTATGT ATATTGTCTA AGTTTCAGCT TTATTGTCTG AAAAAGAAAA TP53 rs1042522 C/G: TGAGGACCTG GTCCTCTGAC TGCTCTTTTC ACCCATCTAC AGTCCCCCTT GCCGTCCCAA GCAATGGATG ATTTGATGCT GTCCCCGGAC GATATTGAAC AATGGTTCAC TGAAGACCCA GGTCCAGATG AAGCTCCCAG AATGCCAGAG GCTGCTCCCC C/G CGTGGCCCCT GCACCAGCAG CTCCTACACC GGCGGCCCCT GCACCAGCCC CCTCCTGGCC CCTGTCATCT TCTGTCCCTT CCCAGAAAAC CTACCAGGGC AGCTACGGTT TCCGTCTGGG CTTCTTGCAT TCTGGGACAG CCAAGTCTGT GACTTGCACG GTCAGTTGCC CTGAGGGGCT MDM2 rs2279744 G/T: GGACTGGGGC TAGGCAGTCG CCGCCAGGGA GGAGGGCGGG ATTTCGGACG GCTCTCGCGG CGGTGGGGGT GGGGGTGGTT CGGAGGTCTC CGCGGGAGTT CAGGGTAAAG GTCACGGGGG CCGGGGGCTG CGGGGCCGCT G/T CGGCGCGGGA GGTCCGGATG ATCGCAGGTG CCTGTCGGGT CACTAGTGTG AACGCTGCGC GTAGTCTGGG CGGGATTGGG CCGGTTCAGT GGGCAGGTTG ACTCAGCTTT TCCTCTTGAG CTGGTCAAGT TCAGACACGT TCCGAAACTG CAGTAAAAGG AGTTAAGTCC TGACTTGTCT KLRK1 rs10772266 A/G: TGTTCATTCA ATATTATATT GGCTATGGGT TTGTCATAAA TAGCTCTTAT CATTTTGAGA TATGTTCCAT CAATGCATAG TTTGAGAGTG TTTTTTTTCT TTTTTTTTTT TAAGGCAAAT GACAAATACC TAGTTTACC A/G TCTTTACTTT TTTAAACCTA ATGTTAACAT TAATATTTAA ACAGTTGTCA AAAATTGCTA AGTTGCCAGC ATTCATGCAC AACTAGAAAA CATCCTTAAC TTATCTTAAA CCAGAAATGT ATTGCCATTA ATGCATTAAT ATCTTTTACT ACTAAATACT GAAAAAAATT GAAATTATTT KLRK1 rs2617160 A/T: ATGCAGGGGC ATCTATGGCC ACACCACCAT GATGCATCCA GTCTCGTCTG GACACGCATG GGCATATTGA AGCAGAAGTG AAATGATGAC TAATGTAAAA GTAAAAAAGT CTGCAAACAT ATTTTAAGAA ATATGTATAT ATATATTTTC AGAACCTATT TTCCATTCAG CTAGGTATTA A/T GTACTGGGCT ACACATACTG ACATATAATG TTAACTGGTG TATTGTAATT ATATGAACTC AAGGCAGAGA TTCCATAAAT CTGGAATTTA TACTTTGGGG AAAAACAGGT CATCATCTTG GCAATTAATT AATTTTCTCT GGCACAGCTT CCTAAGCCAG GAATGATTAA ATGATTTTTT KLRK1 rs2617171 C/G: AAAATGACTT TTCTATAAAA ATAATGAGAT CTTTAAAACA AATATTTTTA AAGCCATTAG CATAAAACTT CACCATCTCT TATAGTATTT GATCTAACCA CTTTCAAAAA TTAATTTGTT TTTCTAAATA TTTTTTCTCT TAAAACATGT CTTTGAGTCA TGAAATCAGA ATACATCTCT C/G TGTGTGTGTA TCATATATAC ATATATATTT AGTACACACA AAAAAATAAA TGTTTTCTAC AATTATTCTG TTATTTATAA ATTTGAAAAG TTCAGAAGCA GCATATTATC TTGGGGTTCA GAGATATACA TTAAACAGAG AATTCTAATC CTCATTATTA TGAAATGTTT CAAGGCGCTT

TABLE 6 Genotyping Methods for Each Single Nucleotide Polymorphism in The SNP Panel SNP Genotyping Method Detail RXRB rs421446 C/T Taqman allelic discrimination ABI Cat No C_27015692_10 RXRB rs2076310 C/T Taqman allelic discrimination ABI Cat No C_16167918_10 BRD2 rs635688 C/T Taqman allelic discrimination ABI Cat No C_3213715_10 HLA-DQA1 rs1142316 A/C PCR-RFLP BglII RFLP analysis HLA-DRA rs7192 G/T Taqman allelic discrimination ABI Cat No C_8848630_20 HSPA1B rs1061581 A/G PCR-RFLP PstI RFLP analysis GTF2H4 rs3909130 A/G Taqman allelic discrimination ABI Cat No C_8941901_10 HLA-E rs1264456 C/T Taqman allelic discrimination ABI Cat No C_8942134_10 HIST1H1T rs198844 G/C Taqman allelic discrimination ABI Cat No C_3266627_10 IRF4 rs12203592 C/T Taqman allelic discrimination ABI Cat No C_31918199_10 IRF4 rs872071 A/G Taqman allelic discrimination ABI Cat No C_8770093_10 LIF rs929271 Taqman allelic discrimination ABI Cat No C_7545904_10 TP53 rs1042522 G/C Taqman allelic discrimination ABI Cat No C_2403545_10 MDM2 rs2279744 T/G PCR-RFLP MspA1I RFLP analysis SLC11A2 rs422982 Taqman allelic discrimination ABI Cat No C_570333_10 SLC40A1 rs1439814 Taqman allelic discrimination ABI Cat No C_2108641_10 RRM2 rs1130609 Taqman allelic discrimination ABI Cat No C_379242_20 TMPRSS6 rs733655 T/C Taqman allelic discrimination ABI Cat No C_3289858_1_ HMOX1 rs2071748 G/A Taqman allelic discrimination ABI Cat No C_2469922_1_ IFNG rs2069727 T/C Taqman allelic discrimination ABI Cat No C_2683475_10 IL6 rs1800796 G/C Taqman allelic discrimination ABI Cat No C_11326893_10 KLRK1 rs10772266 A/G Taqman allelic discrimination ABI Cat No C_9345268_10 KLRK1 rs2617160 T/A Taqman allelic discrimination ABI Cat No C_1841959_10 KLRK1 rs2617171 G/C Taqman allelic discrimination ABI Cat No C_26984346_10 

What is claimed is:
 1. A method for predicting prenatal loss of a conceptus or embryo, comprising the steps of: (a) providing a biological sample from a pregnant woman; (b) isolating a nucleic acid from said biological sample; (c) performing a polymerase chain reaction (PCR) on said isolated nucleic acid to produce an amplicon; (d) assessing said amplicon for the presence of a combination of SNPs, said SNP combination consisting of HLA-DQA1 rs1142316, HLA-DRA rs7192, and HSPA1B rs1061581; and (e) predicting an increased risk of prenatal loss of said male conceptus or embryo in said pregnant woman by said presence of said SNP combination, wherein the presence of said SNP combination is indicative of an increased risk of prenatal loss for male conceptus or embryo.
 2. The method of claim 1, wherein said biological sample is derived from a conceptus or amniocentesis.
 3. The method of claim 1, wherein said nucleic acid is selected from the group consisting of genomic DNA, mRNA and isolated DNA.
 4. The method of claim 1, wherein said assessing step is performed by polymerase chain reaction-restriction fragment length polymorphism assay or TaqMan allelic discrimination assay.
 5. The method of claim 4, wherein said assessing step is performed by polymerase chain reaction-restriction fragment length polymorphism assay.
 6. The method of claim 4, wherein said assessing step is performed by TaqMan allelic discrimination assay.
 7. The method of claim 4, wherein said assessing step is performed by a process which comprises subjecting said isolated nucleic acid to a PCR flanking the region of said SNP.
 8. The method of claim 1, wherein said assessing step is performed on the presence of a SNP further selected from the group consisting of RXRB rs421446, BRD2 rs635688, HLA-E rs1264456, IRF4 rs12203592, IRF4 rs872071, LIF rs929271, TP53 rs1042522, MDM2 rs2279744, SLC11A2 rs422982, SLC40A1 rs1439814, and RRM2 rs1130609.
 9. A method of predicting prenatal survival probability of a prospective offspring of a couple, comprising the steps of: (a) providing a biological sample from a pregnant woman; (b) isolating a nucleic acid from said biological sample; (c) performing a polymerase chain reaction (PCR) on said isolated nucleic acid to produce an amplicon; (d) assessing said amplicon for the presence of a combination of SNPs, said SNP combination consisting of HLA-DQA1 rs1142316, HLA-DRA rs7192, and HSPA1B rs1061581; and (e) predicting a decreased prenatal survival probability of said prospective male offspring of said couple by said presence of said SNP combination, wherein the presence of said SNP combination is indicative of a decreased prenatal survival probability of a prospective offspring.
 10. The method of claim 9, wherein said biological sample is derived from a conceptus or amniocentesis.
 11. The method of claim 9, wherein said nucleic acid is selected from the group consisting of genomic DNA, mRNA and isolated DNA.
 12. The method of claim 9, wherein said assessing step is performed by polymerase chain reaction-restriction fragment length polymorphism assay or TaqMan allelic discrimination assay.
 13. The method of claim 12, wherein said assessing step is performed by polymerase chain reaction-restriction fragment length polymorphism assay.
 14. The method of claim 12, wherein said assessing step is performed by TaqMan allelic discrimination assay.
 15. The method of claim 12, wherein said assessing step is performed by a process which comprises subjecting said isolated nucleic acid to a PCR flanking the region of said SNP.
 16. The method of claim 9, wherein said assessing step is performed on the presence of a SNP further selected from the group consisting of RXRB rs421446, BRD2 rs635688, HLA-E rs1264456, IRF4 rs12203592, IRF4 rs872071, LIF rs929271, TP53 rs1042522, MDM2 rs2279744, SLC11A2 rs422982, SLC40A1 rs1439814, and RRM2 rs1130609. 